Acid recovery with diffusion dialysis to improve rare earth extraction economics
Bibliographic record
Abstract
Acidic Mine Waters (AMWs), rich in dissolved metals and sulphates, contain a significant amount of rare earth elements (REEs), making their recovery attractive. Due to the natural composition of AMWs, ion-exchange (IX) technologies can selectively extract REE, but at the expense of a large excess consumption of sulphuric acid to regenerate the IX resins. As a matter of fact, the higher the sulphuric concentration applied during regeneration, the higher the REE concentration factors achieved in the eluate. This excess of free acid concentration in the eluate makes the REEs recovery both a technical and an economic challenge, due to the large amounts of chemicals needed (i.e., alkali for acidity neutralization and oxalic acid for subsequent REE precipitation). Diffusion dialysis (DD), a membrane-based separation process, has been postulated as an option for the selective recovery of sulphuric acid from the IX eluates, leaving a stream concentrated in REE and other metals rejected by the membrane. This work aimed to obtain the greatest sulphuric acid recovery with the minimum losses of REE by permeation through the DD membrane. An anion exchange membrane (AEM), containing a quaternary ammonium functional group, was used first to characterise ion transport through the AEM in batch configuration, and, second, to assess the effect of flow-rate on acid recovery in dynamic mode. A complex synthetic eluate containing REEs and interfering cations, co-extracted during the IX stage, was used, achieving rejection efficiencies >96 % for major metal ions (Na + , Ca 2+ , Mg 2+ , and Al 3+ ), while maintaining recoveries of sulphuric acid of 78–85 %. This selective transport was governed primarily by Donnan and dielectric exclusion mechanisms, further modulated by ionic charge, hydrated radius, and concentration gradients. The separation resulted in REE-containing solutions with tailored ionic profiles (e.g., sulphuric acid solutions) for subsequent crystallization as oxalates, leading to reduced reagent consumption and higher purity of the solid obtained. • Diffusion dialysis (DD) enables acid recycling, reducing process costs. • Integration of DD in ion-exchange processes for rare earth elements (REEs) recovery • High sulphuric acid recovery (>78 %) via diffusion dialysis • Metal cation rejection exceeds 96 % by Fumasep FAQP-375-PP. • Diffusion dialysis reduces chemical consumption in rare earth processing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".